Machine Learning Methods & Theory

We actively develop new methodology and explore learning theory relevant to applications in medical imaging. Areas of research include causality, domain shift, robustness & reliability, bias & fairness, generative modelling, semi-supervised & self-supervised learning, deep representation learning, and geometric deep learning.


Causal Machine Learning

High Fidelity Image Counterfactuals with Probabilistic Causal Models, International Conference on Machine Learning (ICML), 2023

Measuring axiomatic soundness of counterfactual image models, International Conference on Learning Representations (ICLR), 2023

Deep Structural Causal Models for Tractable Counterfactual Inference, Advances in Neural Information Processing Systems (NeurIPS), 2020

Causality matters in medical imaging, Nature Communications, 2020


Robustness & Reliability

Automatic correction of performance drift under acquisition shift in medical image classification, Nature Communications, 2023

Robustness Stress Testing in Medical Image Classification, MICCAI Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (UNSURE), 2023

Failure Detection in Medical Image Classification: A Reality Check and Benchmarking Testbed, Transactions on Machine Learning Research, 2022

Analyzing Overfitting under Class Imbalance in Neural Networks for Image Segmentation, IEEE Transactions on Medical Imaging, 2021


Representation Learning

Grounded Object-Centric Learning, International Conference on Learning Representations (ICLR), 2024

Domain Generalization via Model-Agnostic Learning of Semantic Features, Advances in Neural Information Processing Systems (NeurIPS), 2019

Semi-supervised learning via compact latent space clustering, International Conference on Machine Learning (ICML), 2018


Fairness & Bias

A causal perspective on dataset bias in machine learning for medical imaging, Nature Machine Intelligence, 2024

Risk of Bias in Chest Radiography Deep Learning Foundation Models, Radiology: Artificial Intelligence, 2023

The Role of Subgroup Separability in Group-Fair Medical Image Classification, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2023

Algorithmic encoding of protected characteristics in chest X-ray disease detection models, eBioMedicine, 2023

Potential sources of dataset bias complicate investigation of underdiagnosis by machine learning algorithms, Nature Medicine, 2022


Machine Learning on Graphs

A Comparative Study of Graph Neural Networks for Shape Classification in Neuroimaging, Proceedings of Machine Learning Research, GeoMedIA Workshop, 2022

Graph Convolutional Gaussian Processes, International Conference on Machine Learning (ICML), 2019

Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer’s Disease, Medical Image Analysis, 2018

Metric learning with spectral graph convolutions on brain connectivity networks, NeuroImage, 2018

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